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Forecasting the 2012 and 2014 Elections Using Bayesian Prediction and Optimization

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  • Steven E. Rigdon
  • Jason J. Sauppe
  • Sheldon H. Jacobson

Abstract

This article presents a data-driven Bayesian model used to predict the state-by-state winners in the Senate and presidential elections in 2012 and 2014. The Bayesian model takes into account the proportions of polled subjects who favor each candidate and the proportion who are undecided, and produces a posterior probability that each candidate will win each state. From this, a dynamic programming algorithm is used to compute the probability mass functions for the number of electoral votes that each presidential candidate receives and the number of Senate seats that each party receives. On the final day before the 2012 election, the model gave a probability of (essentially) one that President Obama would be reelected, and that the Democrats would retain control of the U.S. Senate. In 2014, the model gave a final probability of .99 that the Republicans would take control of the Senate.

Suggested Citation

  • Steven E. Rigdon & Jason J. Sauppe & Sheldon H. Jacobson, 2015. "Forecasting the 2012 and 2014 Elections Using Bayesian Prediction and Optimization," SAGE Open, , vol. 5(2), pages 21582440155, April.
    • Handle: RePEc:sae:sagope:v:5:y:2015:i:2:p:2158244015579724
    DOI: 10.1177/2158244015579724
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    References listed on IDEAS

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    3. Brown, Lloyd B. & Chappell Jr., Henry W., 1999. "Forecasting presidential elections using history and polls," International Journal of Forecasting, Elsevier, vol. 15(2), pages 127-135, April.
    4. Christensen, William F. & Florence, Lindsay W., 2008. "Predicting Presidential and Other Multistage Election Outcomes Using State-Level Pre-Election Polls," The American Statistician, American Statistical Association, vol. 62, pages 1-10, February.
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    Cited by:

    1. Sebasti'an Morales & Charles Thraves, 2020. "On the Resource Allocation for Political Campaigns," Papers 2012.02856, arXiv.org.

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